With the rise of data and growing demand for real-time analytics, new tools have emerged to help businesses make informed decisions. One such tool is small data analytics, also known as Tiny Data, which aims to make data analytics more accessible and easy to use for businesses of all sizes. 

Small Data

What is Small Data?

Tiny Data is an approach to analyzing data that focuses on using small datasets to make decisions. Instead of focusing on large amounts of data, Tiny Data focuses on smaller, more relevant data for decision-making. This may include internal data, such as sales and supply chain management data, as well as external data, such as market data and consumer data.


Tiny Machine Learning: A natural extension of Small Data

Tiny Machine Learning is a natural extension of small data analysis. This is an approach to using machine learning to solve problems using smaller datasets. This can be done without the need for large-scale tools, such as large computing clusters or deep learning frameworks.

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One of the key advantages of small data and tiny machine learning is their portability. Small data algorithms can be run on mobile devices, such as smartphones, smart watches, etc. This makes them ideal for real-time applications that require data analysis on the go. Similarly, machine learning systems can be deployed on connected objects such as smart thermostats, surveillance cameras, alarm systems and connected locks.

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A green solution

Energy consumption in data centres is a growing concern for the environment. According to a Greenpeace study, data centres and cloud computing infrastructures are responsible for 2% of global greenhouse gas emissions, comparable to emissions from the aviation industry. This is largely due to the large amount of energy required to operate these data centres, including cooling the servers.

This is why the use of small data can be beneficial for the environment. By limiting the amount of data processed, it can reduce the amount of energy needed to store and process it. In addition, as Small Data algorithms can be run on mobile devices, this can avoid the need to send data to data centers for processing.

Practical applications

There are several concrete applications for Tiny Machine Learning, including:

  • Sales Prediction: Companies can use Tiny Machine Learning algorithms to predict sales using historical sales data and other relevant factors.
  • Supply Chain Optimization: Companies can use Tiny Machine Learning to optimize their supply chain using data such as demand data, production data, and delivery data.
  • Fraud detection: Businesses can use Tiny Machine Learning to detect fraud using data such as banking transactions and payment data.
  • Real-time data analysis: Small data algorithms can be used for real-time data analysis on mobile devices, such as connected watches.
  • Health surveillance: Small data algorithms can be used for health surveillance on mobile devices, such as connected wristbands.
  • Computer vision applications: Small data algorithms can be used for computer vision applications on mobile devices, such as cameras.


“What is Small Data?” Forbes, Forbes Media LLC, 25 May 2018, www.forbes.com/sites/bernardmarr/2018/05/25/what-is-small-data-the-next-big-thing-in-big-data/?sh=1c76465c1a24.

“Tiny Machine Learning on Small Data: An Overview.” Medium, Towards AI, 4 Oct. 2019, towardsai.net/tiny-machine-learning-on-small-data-an-overview. 

“Small Data: How to Make Big Impact.” The Guardian, Guardian News and Media, 6 June 2018, www.theguardian.com/sustainable-business/2018/jun/06/small-data-make-big-impact. 

“The Advantages and Disadvantages of Tiny Machine Learning.” Medium, Springboard, 22 May 2018, medium.springboard.com/the-advantages-and-disadvantages-of-tiny-machine-learning-2e0f0e91b1c8.